Predicting home prices accurately poses a significant challenge due to various influencing factors like property attributes, location, economic conditions, and market dynamics. Buyers rely on precise estimates to make informed investment decisions, ensuring they secure fair deals without overpaying. Sellers benefit from understanding their property's value, allowing them to set competitive prices and maximize profits.
Develop robust house price prediction models. These models, by considering a multitude of variables including property features, economic indicators, and market trends, aim to provide accurate predictions aligned closely with actual sales prices
Importing Libraries and Data Loading EDA - Data Cleaning. Outlier Removal Training and Testing Model Training, Machine Learning Regression Feature Engineering and Feature Importance Recommendations Neaural Network Regression using TensorFlow. Conclusion
Positive correlations were observed, such as: OverallQual vs. SalePrice: The overall quality of materials and finishing is positively correlated with the sale price.
GrLivArea vs. SalePrice: The above-ground living area also shows a positive correlation, indicating that homes with more living space tend to have higher prices.
GarageArea vs. SalePrice: The garage area has a positive relationship with the sale price.
YearBuilt vs. SalePrice: The year built is also positively correlated with the sale price.
GarageYearBuilt vs. SalePrice: The Garage year built has a positive correlation with the sale price.